From the builder of the Step Pyramid, Imhotep to Elon Musk, engineers have made a real impact on our day-to-day lives. On Engineer’s Day, we evaluate how the role of engineers has evolved over these years.

Impact Of Emerging Technologies in Engineering

Emerging technologies like artificial intelligence, machine learning, cloud computing, blockchain, among others have heavily impacted the industry over the past five years. Today, engineers are tasked with providing advanced approach in an organisation that can deliver a business impact and enhanced ROI. In the future, job profiles like autonomous transportation specialists, technology advocates, augmented reality developers, etc. will be trending in IT sector.

How the skill-set has evolved

As we know “Change is the only constant.” There was a time when programming skills and knowledge of Java, C, among others, understanding the software models was enough to land a good job. With digital transformation becoming a buzzword in organisations, recruiters are looking to fill generalist and specialist roles in the market. These job roles command hefty packages and also require a well-rounded functional knowledge.

Most Hired Sector

Of late, recent news indicates that automation, especially on warehouse floors will cut down jobs. For now, it seems that these technologies have opened doors and opportunities to various job roles. According to sources, the Indian IT industry is expected to add around 2.5 lakh new jobs in 2019, further contributing to the growth of the sector. Areas where an uptake in hiring is expected are computer, mathematical, architecture and engineering-related fields. Currently, some of the prominent roles in demand are software developers, information security analysts, machine learning, mobility, cloud engineer, DevOps, network analyst, data scientist, data analyst, and cybersecurity experts.

Dark Side

Every coin has two sides. Due to the digitalisation, there has been a drastic change in the jobs in the IT sector. Recently, tech companies have been laying off employees in order to ramp up resources with a specialised skillset.

In May 2019, tech giant IBM laid off nearly 300 employees from its services division as the tech giant looked to “re-invent itself ” and meet the changing needs of customers. According to reports, a majority of these employees were in software services roles. They were let go as IBM focuses on emerging technology capabilities and reduces exposure to traditional services.

Recently, popular food delivery startup, Zomato laid off 540 employees at its head office in Gurugram across its customer, merchant, and delivery partner support teams. The management team expanded the reason as it is due to the fact that the company is fast expanding and trying to optimise its costs through automation.

]]>Why Does France Think That Facebook’s Libra Project Is A Big Threat?https://www.analyticsindiamag.com/france-facebook-libra-blockchain/
Sat, 14 Sep 2019 09:06:32 +0000https://www.analyticsindiamag.com/?p=45940France has assumed a dominant role in regulating large tech corporations companies in Europe, mandating a 3% digital tax on US firms like Facebook, Amazon and Google. Now, the country has announced that it would block the launch of Facebook’s Libra digital currency as may threaten Europe’s sovereignty. Finance Minister Bruno Le Maire said Libra…

France has assumed a dominant role in regulating large tech corporations companies in Europe, mandating a 3% digital tax on US firms like Facebook, Amazon and Google. Now, the country has announced that it would block the launch of Facebook’s Libra digital currency as may threaten Europe’s sovereignty. Finance Minister Bruno Le Maire said Libra has a potential of risks and has room to be abused. While Maire highlighted the concerns, he did not elaborate on how France would keep Libra out of the 28-member European Union. The announcement brought yet another bad news for Facebook in less than a month after the EU launched anantitrust investigation into the Libra project.

Maire highlighted that Libra will not be decentralised like other digital currencies but instead be controlled by a small group of private companies. “This eventual privatisation of money contains risks of abuse of dominant position, risks to sovereignty, and risks for consumers and for companies,” Maire said. He also stated Libra could create a risk of a global crisis in the event of a technical bug. Pointing to the impact that project Libra may have, Maire also added that France has a legal framework for using blockchain technology but in case the emergence of new technologies pose risk to government sovereignty, then it will happen without France.

The European Commission has responded to Le Maire’s announcement, stating it would also research the various issues with Libra, ranging from tax complexities to data privacy concerns. In response, the Libra Association said it is working with regulators around the globe and wouldn’t proceed without regulations are met. “The Libra Association and its members are committed to working with regulatory authorities to achieve a safe, transparent, and consumer-focused implementation of the Libra project. We recognise that blockchain is an emerging technology and that policymakers must carefully consider how its applications fit into their financial system policies,” Dante Disparte, Head of Policy and Communications for the Libra Association said in response.

User Data Privacy and Monetary Trust Is The Biggest Challenge For Libra

The social media giant announced plans for Libra currency in July but since then it has faced a huge backlash from authorities around the world.Experts have pointed out that Facebook Libra is not a decentralised network like other digital currencies like Bitcoin or Ethereum, where no one body or organisation controls the technology. This opens the door for the misuse of financial data collected through the network. Authorities say the risks are not limited to financial privacy since the involvement of Facebook and its expansive categories of data collection on hundreds of millions of users raises additional concerns.

In the past, incidents like theCambridge Analytica exposed how data of millions of users was being harvested on Facebook. The company’s misuse in the spread of fake news and US election meddling has also come under heavy scrutiny. Facebook has tried to reassure by saying it will keep financial and social data completely separate and users will not be targeted with adverts based on their spending habits.

Trust Issues May Hamper The Libra Project Going Forward

Libra came into existence after the collaboration of big tech and financial players in payments such as Uber, Lyft, Vodafone, PayU, Andreessen Horowitz, eBay, Spotify, Paypal and Visa, Uber, Lyft and others under the leadership of Facebook’s arm Calibra. It is one of the biggest such partnerships in the world of blockchain. In the past, Facebook rolled out global level technology initiatives in the past likeInternet.org which had social good intentions of providing basic services but was later criticised to only promote the interests of Facebook. Now with Libra, Facebook says that it wants to make global payments as easy as a click of a button without a bank account and at minimal costs. While it sounds like a great initiative that can bring financial inclusion to billions of people, experts and authorities are concerned about Facebook’s real intentions.

]]>AI In Enterprises Is Clearly Going Through A Flux, But The Future Looks Brighterhttps://www.analyticsindiamag.com/ai-in-enterprises-challenges-opportunity/
Sat, 14 Sep 2019 04:30:18 +0000https://www.analyticsindiamag.com/?p=45944The best of machine learning and deep learning models depend on a large amount of processing power so that a series of calculations are done in as little as microseconds or nanoseconds. However, there isn’t enough processing power across enterprises to implement ideal artificial intelligence techniques, which poses a challenge for advanced implementations. To tackle…

The best of machine learning and deep learning models depend on a large amount of processing power so that a series of calculations are done in as little as microseconds or nanoseconds. However, there isn’t enough processing power across enterprises to implement ideal artificial intelligence techniques, which poses a challenge for advanced implementations. To tackle this, cloud computing and massively parallel processing systems are needed for enterprises to deploy AI systems that are scalable in accordance with the rising volume of data.

A reportsuggests that by 2020, 85% of CIOs will be piloting enterprise AI projects with a combination of buying, building and outsourcing initiatives. As the subject of democratisation of AI and deep learning technologies keep popping up in a variety of industries, the demand for AI professionals has become very high. The problem is that there’s a clear shortage of skills in data science, robotics and AI engineering. Even though there are about 300,000 AI professionals across the world, the number of available job roles for such professionals run in millions, according toestimates. In fact, staffing skills is the number one challenge for 54% of CIOs looking to adopt AI, says one report.

Data Plays A Crucial Role For AI To Thrive

It goes without saying that the accessibility to ‘right data’ plays a crucial role in creating the right AI model. With the tremendous volume and velocity of data in the enterprise, one of the biggest challenges is to be able to make sense of it all to drive profitable business decisions. Too much data can take the focus away from actionability and can cause data paralysis. It is important to capture data and correct the noise to have the right data strategy so AI models can be trained in the most efficient manner. However, datasets that are relevant for AI applications to learn are often rare. The most powerful AI machines are the ones that are trained on supervised learning. This training relies on labelled data – data that is organised to make it work for machine learning, and such labelled data is limited in the enterprise.

To take advantage of AI requires that enterprises integrate all the relevant data. AI systems cannot derive ad hoc insights unless the data is quantified and inserted in a data pipeline which is then connected into the model itself. In any organisation, there can be thousands of disparate streams of data or metrics. To make data streams converge at a single data warehouse or data lake is a big challenge. Gartner research has found that organisations report poor data quality may cause an average of $15 million per year in losses. The situation may actually become worse as data comprising of different formats become increasingly complex — a challenge faced by organisations of all sizes. Businesses with many units and operations spread out in varying geographies may experience more severedata quality issues.

One of the other issues is an assumption that AI does not need governance or being supervised: the assumption that it is all well-programmed and will work forever. But any change in a business model, company’s policy or government regulation can impact artificial intelligence systems and therefore a governance layer is always needed. Organisations need to monitor the outcomes of AI-driven projects and refine them to drive improved outcomes over time.

AI Innovation Does Not Mean Infringement Of Privacy

Another challenge that businesses face has to do with user privacy. According to PwC research, people have concerns regarding data privacy to share data even for a better experience. A vast majority of respondents (93%) reported their hesitance to share personal data such as medical records. At a time when large tech corporations have come under fire for violations of data privacy, it is therefore imperative for businesses to be compliant with the regulations. Privacy laws can hamper the way AI models are trained. Regulations like GDPR have clauses which oblige companies to provide either detailed explanations of individual algorithmic decisions or general information about how the algorithms make decisions, which can make it difficult for companies to adopt AI.

With all the efficiencies that exist today, enterprises are looking for different ways in which processes can be made more efficient, costs can be reduced and decision making can be enhanced. This is where AI can be of great help. Today, exponentially more computing power has become available to train larger and more complex AI models. With the ability to run millions of simulations rapidly, or analyse large volumes of historical data, AI systems can detect non-obvious variables that have the predictive capability and leverage them to acquire a competitive advantage.

Enhancement of customer experience is one of the biggest opportunities which businesses aim to achieve using AI systems. The highly repetitive customer interactions can be better handled by automated systems using natural-language processing. For an e-commerce business, chatbots, either through voice or email can resolve the extremely large volume of customer requests. Analysing unstructured data from multiple sources across social media and web channels, AI systems can present actionable insights on the data in real-time.

Businesses need predictability of functions and in this context, AI-powered recommendation systems have a prominent role to play in driving revenue growth. For example, AI can add value for sales through contextual recommendations using data in customer interactions across different channels. AI-driven processes can also be applied to a number of related areas at the same time. Consider the departments of an e-commerce company where an AI system trained on customer data and preferences from one department, can also be applied to other departments in making the right decisions. The goal is to optimise process efficiency by eliminating pain points using artificial intelligence.

]]>A New Perspective On Grover’s Search Algorithm — Connecting Quantum Physics & DNA https://www.analyticsindiamag.com/grover-search-algorithm-dna-electrons-quantum-computer/
Fri, 13 Sep 2019 13:06:44 +0000https://www.analyticsindiamag.com/?p=45934Finding the shortest path or the most optimised path is prevalent in biological systems. Ants don’t just fancy a random rendezvous in search for food. Many biological systems barring humans are quite efficient in the conservation of energy, in carrying out their routines. A close similarity can be found in the way computers work. The…

Finding the shortest path or the most optimised path is prevalent in biological systems. Ants don’t just fancy a random rendezvous in search for food. Many biological systems barring humans are quite efficient in the conservation of energy, in carrying out their routines.

A close similarity can be found in the way computers work. The task can be as primitive as searching databases for telephone numbers or breaking cryptographic codes, the algorithms try to complete the task as quickly as possible. In fact, many algorithms, directly or indirectly, have taken inspiration from biological systems.

So, a task in the context of machines, is assessed for speed by counting the steps it takes to end. Computer scientists have always considered that a process takes around N steps because in the worst case, the last item on the list could be the one of interest.

However, Lov Grover, a physicist, showed in 1996, how the strange rules of quantum mechanics allowed the search to be done in a number of steps equal to the square root of N.

A classical (or non-quantum) algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer.

In quantum computing, a quantum algorithm is an algorithm which runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation.

In computational complexity theory, a problem is NP-complete when it can be solved by a restricted class of brute force search algorithms and it can be used to simulate any other problem with a similar algorithm.

More precisely, each input to the problem should be associated with a set of solutions of polynomial length, whose validity can be tested quickly (in polynomial time), such that the output for any input is “yes” if the solution set is non-empty and “no” if it is empty.

Amongst all quantum algorithms, the reasons to focus on the Grover search are as follows:

because of its remarkable generality, as it speeds up any brute force O(N) problem into an O (√N) problem.

because of its remarkable robustness.

Imagine a phone directory containing N names arranged in a completely random order. In order to find someone’s phone number with a 50% probability, any classical algorithm (whether deterministic or probabilistic) will need to look at a minimum of N/2 names.

Quantum mechanical systems can be in a superposition of states and simultaneously examine multiple names. Grover in his paper proposes that by properly adjusting the phases of various operations, successful computations reinforce each other while others interfere randomly.

As a result, the desired phone number can be obtained in only O(sqrt(N)) steps. Grover’s algorithm is within a small constant factor, was considered to be the fastest possible quantum mechanical algorithm back then.

A New Perspective On Grover’s Algorithm

Source: MIT

Grover’s work was an important factor in preparing for the world of quantum computing, which is still in its infancy.

The first quantum computer capable of implementing it appeared in 1998, but the first scalable version didn’t appear until 2017.

Today, a team of researchers from France say they have evidence that Grover’s search algorithm is a naturally occurring phenomenon. They claim to have observed this behaviour in electrons.

Grover’s search algorithm can be reformulated in a variety of ways. One of these is as a quantum walk across a surface—the way a quantum particle would move randomly from one point to another.

The team focused on simulating the way a Grover search works for electrons exploring triangular and square grids as shown above.

The objective of this study can be summarized as finding how quickly an electron can find the hole in a grid. And the team’s big breakthrough is to show that these simulations reproduce the way real electrons behave in real materials.

Implications Of These Findings

Photo By Yomex Owo For Unsplash

The researchers at Universit́e de Toulon based on their observations, say that free electrons naturally implement the Grover search algorithm when moving across the surface of certain crystals. This has immediate implications for quantum computing. For instance, this can be applied to solve correct the errors in a full-scale quantum computer.

The work also has implications for our thinking about the genetic code and the origin of life. Every living creature on Earth uses the same code, in which DNA stores information using four nucleotide bases. The sequences of nucleotides encode information for constructing proteins from an alphabet of 20 amino acids.

Back in 2000, Apoorva Patel of IISc Bengaluru showed how Grover’s algorithm could explain the numbers 4 and 20.

Patel showed that when there are four choices, a quantum search can distinguish between four alternatives in a single step. Indeed, four is optimal number.

However, biologists were dismissive of these results, saying that quantum processes couldn’t possibly be at work inside living things.

Now, these latest observations of the French researchers, can fortify two decades old observations of Patel and throw some light on how life itself finds a way.

A century ago, no would have believed that atoms can be manipulated to build computers and the algorithms designed to make these computers better can be used to decode how life springs up.

If this algorithm is really what the researchers say it is then we can safely assume that life is just an example of Grover’s algorithm at work !

]]>Top 5 Papers By Turing Award Winner Yoshua Bengio That Push The Boundaries Of AIhttps://www.analyticsindiamag.com/top-5-papers-by-turing-award-winner-yoshua-bengio-that-push-the-boundaries-of-ai/
Fri, 13 Sep 2019 12:30:42 +0000https://www.analyticsindiamag.com/?p=45930Yoshua Bengio is recognised as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Following his studies in Montreal, culminating in a PhD in computer science from McGill University in 1991, Professor Bengio did postdoctoral studies at the Massachusetts Institute of Technology (MIT) in Boston. In 2019, he was…

Yoshua Bengio is recognised as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Following his studies in Montreal, culminating in a PhD in computer science from McGill University in 1991, Professor Bengio did postdoctoral studies at the Massachusetts Institute of Technology (MIT) in Boston.

In 2019, he was awarded the Killam Prize as well as the 2018 Turing Award, considered to be the Nobel prize for computing. These honours reflect the profound influence of his work on the evolution of our society.

Yoshua Bengio is also known for collecting the largest number of new citations in the world in the year 2018. Here are a few of his works, which have pushed the boundaries of AI:

Learning Long-Term Dependencies With Gradient Descent Is Difficult

Cited by: 3896 | Published in 1994

This work by Bengio and his colleagues is a testimony to all the accolades he has garnered over the years. This paper is an extraordinary treatise into the practical shortcomings of Recurrent Neural Networks(RNNs). RNNs were barely popular in the early 90s and Bengio already had discussed in detail why gradient based algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases.

Today, RNNs are popular in the form of LSTMs. From speech assistants to handwriting recognition to music compositions, one cannot ignore their presence.

Convolutional Networks For Images, Speech, And Time Series

Cited by: 2433 | Published in 1995

In this seminal paper, Bengio collaborated with Lecun to uncover the reach of CNNs. Today, manu machine vision tasks are flooded with CNNs. They are the workhorses of autonomous driving vehicles and even screen locks on mobiles.

This work discusses about the variants of CNNs addressing the innovations of Geoff Hinton and Yann Lecun while also indicating how easy it is to implement CNNs on hardware devices dedicated to image processing tasks.

Gradient based Learning Applied To Document Recognition

Cited by: 20630 | Published in 1998

The main message of this paper is that better pattern recognition systems can be built by relying more on automatic learning and less on hand designed heuristics.

Yoshua Bengio along with fellow Turing award winner Yann Lecun, demonstrate that show that the traditional way of building recognition systems by manually integrating individually designed modules can be replaced by a well principled design paradigm called Graph Transformer Networks that allows training all the modules to optimise a global performance criterion.

Learning Deep Architectures For AI

Cited by: 7070 | Published in 2009

This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

This work is a detailed report on the then state-of-the-art architectures. This report poses open questions to the shortcomings of few architectures while also suggesting new avenues for optimising deep architectures, either by tracking solutions along a regularisation path, or by presenting the system with a sequence of selected examples illustrating gradually more complicated concepts, in a way analogous to the way students or animals are trained.

Neural Machine Translation by Jointly Learning To Align And Translate

Cited by: 8231 | Published in 2014

In this new approach, the authors achieved a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation.

This work addressed the drawbacks of traditional encoder-decoder approach and allows the model to focus only on information relevant to the generation of the next target word instead of having to encode a whole source sentence into a fixed-length vector.

This paper led to better machine translation models and a better understanding of natural languages in general.

]]>A Day In The Life Of: A CTO Who’s Running A Bootstrapped Startup & Is In Love With Codinghttps://www.analyticsindiamag.com/a-day-in-the-life-of-a-cto-whos-running-a-bootstrapped-startup-is-in-love-with-coding/
Fri, 13 Sep 2019 12:14:39 +0000https://www.analyticsindiamag.com/?p=45928In our weekly column A Day In The Life Of, we are trying to step into the shoes of awesome techies from various organisations and sectors who are working in emerging tech areas like big data, data analytics, artificial intelligence, machine learning and the internet of things, among others. This week Analytics India Magazine got…

In our weekly column A Day In The Life Of, we are trying to step into the shoes of awesome techies from various organisations and sectors who are working in emerging tech areas like big data, data analytics, artificial intelligence, machine learning and the internet of things, among others.

This week Analytics India Magazine got in touch with Ramki Gaddipati the CTO and co-owner of Bengaluru-headquartered Zeta.

As a health nut, Gaddipati starts his day with a healthy dose of his favourite workout, pilates. Being an entrepreneur in a city like Bengaluru, he is used to the daily routine of traffic, followed by the ups and downs of a dynamic corporate environment.

However, Gaddipati does find a way to have fun. “We are a bootstrapped startup, and I started with a very close friend of mine. And then more friends joined us to get it where it is today. So yes, when I look at my social life it is very active, as I am always around friends at work,” he says, chuckling.

When asked about what his daily work consists of, Gaddipati says that when one is setting up a company, one cannot escape from additional work like management, hiring, funding, sales and more. However, he is very passionate about coding and cherishes those moments when his work involves coding.

Gaddipati and his team are currently working on a few global launches of which US is the predominant one. Earlier this year, Zeta and Sodexo Benefits and Rewards Services (BRS) India, merged their corporate and meal benefits operations into a single entity. Through the merger, Zeta has been pushing their business into the Sodexo family for a minority share in the new entity, which will retain the Sodexo branding. The company will also get a board seat in the merged company. Zeta had also hived off its technology entity, which now operates as a separate unit based out of Bengaluru.

When asked about the current challenges that Gaddipati faces as a CTO, he replies promptly, “The current challenge I’m facing is hiring the right talent for the right team.”

But the best part about his workday is when he’s solving problems with his team. “Anytime we are solving technical problems or having a general technical discussion is the best part of my day. So primarily any technical huddle is what I enjoy the most and given my schedule this is usually 5 – 6 days times a day, making the day better for me,” he says laughing.

However, the most tedious of his day is when he has to “translate” the same problem for different teams. “One of the most challenging parts of my day is modifying my conversations for a different set of audiences. For example, explaining a product to a technical team and then to a sales and marketing team. The topic remains the same, but the challenge is to make both the audience understand it,” he says.

As a techie working in FinTech, Gaddipati says that the contribution that a tech person can make to a company never really ends. “I will continue to innovate with my team and come up with products that not only will expand our reach but will also establish us as one of the preferred partners for all its existing and potential customers,” he says.

]]>Top 10 Scala Libraries For Data Sciencehttps://www.analyticsindiamag.com/top-10-scala-libraries-for-data-science/
Fri, 13 Sep 2019 11:30:47 +0000https://www.analyticsindiamag.com/?p=45925Scala or Scalable language is an extension of Java language which runs on Java Virtual Machine (JVM). It is one of the de facto languages when it comes to playing practically with Big Data. This statically-typed language serves as an important tool for the data scientists because it supports both anonymous functions as well as…

Scala or Scalable language is an extension of Java language which runs on Java Virtual Machine (JVM). It is one of the de facto languages when it comes to playing practically with Big Data. This statically-typed language serves as an important tool for the data scientists because it supports both anonymous functions as well as higher-order functions. In this article, we list down 10 Scala Libraries for a data science enthusiast.

(The list is in alphabetical order)

1| Breeze

Breeze is a set of libraries for machine learning and numerical computing and is a part of ScalaNLP umbrella project. It is a library for numerical processing which is modelled on Scala. It provides a set of libraries for ScalaNLP which includes linear algebra, numerical computing, and optimisation. It aims to enable a generic, powerful yet still efficient approach to ML.

2| Breeze-viz

Breeze-viz is a visualisation library which is backed by Breeze for Scala. This prominent Java charting library, JFreeChart as well as the Matlab-like “image” command.

3| DeepLearning.scala

DeepLearning.scala is a deep learning toolkit for Scala which combines object-oriented and functional programming constructs. It is a simple library for creating statically typed dynamic neural networks from map/reduce and other higher-order functions. Using this library, writing of code is almost the same and the only difference is that the code based on this library is differentiable which enables such code to evolve by modifying its parameters continuously.

4| Epic

Epic is a structured prediction framework for Scala which includes classes for training high-accuracy syntactic parsers, part-of-speech taggers, named entity recognisers, and much more. It is distributed under the Apache License, Version 2.0 and can be used programmatically or from the command line, using either pre-trained models or with models that a developer has trained by himself. Epic has support for three kinds of models which are parsers, sequence labellers, and segmenters. Parsers produce syntactic representations of sentences, sequence labellers are sort of part-of-speech taggers and segmenters break a sentence into a sequence of fields.

5| Apache PredictionIO

Apache PredictionIO is an open-source machine learning server built on top of a state-of-the-art open source stack for developers and data scientists in order to create predictive engines for any machine learning task. PredictionIO chose Scala as its JVM language over Java primarily because of the advantages it brings to functional programming. This ML server allows to quickly build and deploy an engine as a web service on production, respond to dynamic queries in real-time, speed up machine learning modelling with systematic processes and pre-built evaluation measures, simplify data infrastructure management and much more.

6| Saddle

Saddle is a high-performance data manipulation library for Scala which provides array-backed, indexed, one and two-dimensional data structures, vectorised numerical calculations, automatic data alignment as well as robustness to missing values. It is licensed under Apache License version 2.0 and is said as the easiest and most expressive way to program with structured data on Java Virtual Machine (JVM).

7| ScalaLab

ScalaLab is an efficient scientific programming environment for the Java Virtual Machine (JVM). The main potential of the ScalaLab is numerical code speed and flexibility. Also, a major design priority of ScalaLab is its user-friendly interface. The MATLAB-like mathematical domain-specific language of ScalaLab is termed as ScalaSci which is developed as an internal domain-specific language.

8| Smile

Statistical Machine Intelligence and Learning Engine (SMILE) is a fast and comprehensive machine learning engine. Smile provides hundreds of advanced algorithms with a clean interface and one is able to write applications quickly in Java, Scala, or any JVM languages. Scala API also offers high-level operators that make it easy to build machine learning apps. This engine covers almost every aspect of machine learning techniques such as classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithm, among others.

9| Summingbird

Summingbird is a library which allows writing MapReduce programs that look like native Scala or Java collection transformations and execute them on a number of well-known distributed MapReduce platforms, including Storm and Scalding. The Summingbird program can be executed in batch mode using Scalding while it can be executed in real-time mode using Storm.

10| Vegas

Vegas is a data visualisation library in Scala. It a Scala API for declarative, statistical data visualisations where once is able to work with data files as well as Spark DataFrames and perform filtering, transformations, and aggregations as part of the plotting specification. This library works by compiling down the Scala code into strongly typed JSON specifications.

]]>PepsiCo Sets Up Biz Hub In Hyderabad, Two More MNCs To Followhttps://www.analyticsindiamag.com/pepsico-sets-up-biz-hub-in-hyderabad-two-more-mncs-to-follow/
Fri, 13 Sep 2019 10:47:45 +0000https://www.analyticsindiamag.com/?p=45914Putting up a competitive front for Bengaluru, the south Indian city of Hyderabad is upping its ante as a hub for emerging tech companies. After housing research and development labs for companies such as Adobe, State Street, PayPal in the last few months, the city is now set to have two more large-scale global in-house…

Putting up a competitive front for Bengaluru, the south Indian city of Hyderabad is upping its ante as a hub for emerging tech companies. After housing research and development labs for companies such as Adobe, State Street, PayPal in the last few months, the city is now set to have two more large-scale global in-house centres for major MNCs.

Jayesh Ranjan, the IT Principal Secretary told a leading news daily, “You will see two big names launching their GICs here in October and November. This will further strengthen the Brand Hyderabad. Availability of talent and the presence of an enabling ecosystem are the key reasons why Hyderabad is being preferred.”

Reportedly, these two big names will launch their GICs in the city shortly. In fact, one of them is a Fortune 500 company that operates in the legal domain. The other one is related to stock exchanges.

Beverage giant PepsiCo is also setting up one of its largest business services hubs in Hyderabad. Reportedly, the company has taken up 3.8-lakh square feet of space in IT SEZ Laxmi Infobahn, and plans to hire 2,500 people over the next three years.

In 2019, Hyderabad saw the highest annual increase in median salaries for analytics professionals at almost 5% — from 10.2 lakh in 2018 to 10.7 lakh.

]]>Behind The Code: An RPA Developer Who Knows How Automation Is Creating More Jobshttps://www.analyticsindiamag.com/behind-the-code-an-rpa-developer-who-knows-how-automation-is-creating-more-jobs/
Fri, 13 Sep 2019 09:30:08 +0000https://www.analyticsindiamag.com/?p=45918For our weekly developer column ‘Behind The Code’, we interact with the developer community in India and try to take a look at their journey till date — the way they work, and the tools they use. For this week, we got a chance to interact with Piyush Ratnam, an Application Developer at McAfee. Piyush…

For our weekly developer column ‘Behind The Code’, we interact with the developer community in India and try to take a look at their journey till date — the way they work, and the tools they use. For this week, we got a chance to interact withPiyush Ratnam, an Application Developer at McAfee. Piyush gave us an insight into the RPA industry in India and how he is doing his part in this ever-growing industry.

The Journey

A graduate with a bachelor’s degree in computer science and engineering, Piyush has always been passionate about programming and implementing logics. It all started when he first across Semantic Web and Semantic Search, technology which have roots in early Artificial Intelligence.

Ratnam started his career as an Automation Test Engineer, where he got the exposure to Selenium along with several other programming languages such as Java, JavaScript, C# and Python. Being a tech enthusiast, he has always been inquisitive and thoughtful about Robotics and Machine Learning. And to top his enthusiasm, he got the opportunity to work on Robotic Process Automation (RPA) in McAfee a year back.

With over 4 years of experience, currently, Ratnam is designated as Application Developer in McAfee. His primary role is to develop the bots for the users and to identify the key opportunity areas where it can inculcate the RPA to improve efficiency and productivity in the organisation. Along with that, his role is to maintain various automated dashboards which provide insights to the security groups and to the business.

When asked about the challenges he faced, Ratnam said that there weren’t much of challenges in learning RPA through modules. However, there were challenges when he started with the very first process of implementation, “we didn’t think of various scenarios and cases to be taken care of. And the major challenge was, we could not identify the limitations like access privileges and security concerns.”

But, keeping all the possibilities handled, Ratnam along with the team covered all the cases and got the right access and approvals. They created a run book and process flow charts to cover all the possibilities, thus they overcame and had the first reusable and optimised bot running in Production.

Take On RPA Industry

According to areport, RPA software revenue has grown from 63.1% in 2018 to $846 million, making it the fastest-growing segment of the global enterprise software market. Further, it is also prophesied that the RPA software revenue would reach $1.3 billion this year.

“I recently attended a conference where Microsoft along with Automation Anywhere came up with Auto Insurance Claim Process, and likewise there are many areas like Hypothecation Removal, Loan Approval etc.,” said Ratnam. Also, he said he data privacy and protection would be the major key areas which are expected to get stronger.

Talking about the fear regarding job loss due to automation, Ratnam agrees that there is tremendous fear regarding job security. However, this is fear is valid until people have the misconception that automation can do human work and it can take away their Jobs. There is a difference — automation can reduce human work which is repeated and tedious and allows them to do much better and interesting work.

For example, the computer was developed to carry out calculations and simpler tasks programmatically and let humans not to worry about such tasks this doesn’t mean that it has taken their work and let them jobless. “We must understand there is always technological shift and advancement which results in great achievements. Automation is such shift which has made the work to go with ease and smooth for many of us, in fact, it has created lots of scopes and new job opportunities,” Ratnam added.

Ratnam’s Toolkit

While Ratnam walked us through his journey in RPA, we ask him about his developer toolkit and the programming languages. Ratnam studied the basics of C, C++ during his school days, learnt Java programming during engineering and started his career as Java professional. Later, he then learnt C# as it was required during the Job. Further, when asked about the top coding languages, Ratnam said it is Python — as it is one of the most useful languages for ML and AI.

Talking about the toolkit, Ratnam has worked on various Selenium Automation projects and frameworks like Cucumber, RestAssured, RestSharp etc. But currently, he is working as an RPA developer using Automation Anywhere tools.

“Availability of tools like Automation Anywhere, UiPath, BluePrism etc.has made RPA implementation very easy,” said Ratnam. “These tools do not require much technical knowledge, they are easy to use, anyone can learn and implement RPA with e-Learnings, practice and hands-on.”

Moreover, Ratnam is learning Python Programming. Apart from it, he is also getting exposure to IQ bot.

Key To A Successful RPA Journey

Try to complete all online training and e-learning.

Interact with lots of RPA experts.

Check out all the pre-developed Bots available on Bot Store.

Most importantly, before working on any process, must identify whether it’s the right candidate for RPA or not. Choosing the right process is very essential for successful implementation.

Create process flow charts and run books to cover all the possible scenarios, work with concern teams and get sign off on requirements first.

Be motivated and Aim high. You’re never too old to start learning, and you’re never too young to aim high and achieve great things.

The Future Roadmap

Looking into the future, Ratnam would want to learn SAP Automation using RPA. “I would like to work on Machine Learning and Deep Learning concepts and would look forward to implementing Artificial Intelligence,” Ratnam concludes.

]]>5 Companies That Are Thriving After Open-Sourcing Their AI Tools & Algorithmshttps://www.analyticsindiamag.com/5-companies-that-are-thriving-after-open-sourcing-their-ai-tools-algorithms/
Fri, 13 Sep 2019 08:00:36 +0000https://www.analyticsindiamag.com/?p=45915Open-sourcing has become a tradition for big tech companies today. This not only helps the developers in the community to gain an understanding of the innovative technologies used by the tech giants but also helps the companies to find bugs and make enhancements to the software. In this article, we list down 5 companies who…

Open-sourcing has become a tradition for big tech companies today. This not only helps the developers in the community to gain an understanding of the innovative technologies used by the tech giants but also helps the companies to find bugs and make enhancements to the software. In this article, we list down 5 companies who have been open-sourcing their algorithms and software into the developer community.

1| LinkedIn

Last month, the professional social networking site open-sourced a machine learning library known as the Isolation Forest. The library is being used by the Anti-Abuse AI Team at LinkedIn creates, deploys, and maintains models that detect and prevent various types of abuse, including the creation of fake accounts, member profile scraping, automated spam, and account takeovers.

The developers at LinkedIn created a Spark/Scala implementation of the Isolation Forest unsupervised outlier detection algorithm and is currently available on GitHub. This library supports distributed training and scoring using Spark data structures. This library also supports model persistence on the Hadoop Distributed File System (HDFS). The Isolation Forest algorithm is mainly chosen for vat=rious reasons such as this algorithm is a top-performing unsupervised outlier detection algorithm, scalable, low memory requirements, among others.

2| Microsoft

In March 2019, tech giant Microsoft open-sourced Project Zipline compression algorithms, hardware design specifications, and Verilog source code for register transfer language (RTL) with initial content at the Open Compute Project (OCP) Global Summit 2019. The researchers at Microsoft developed a cutting-edge compression algorithm and optimised the hardware implementation for the types of data that are found in the cloud storage workloads.

Project Zipline compression algorithm yields result up to 2X high compression ratio which is better than the result of commonly used Zlib-L4 64KB model. Project Zipline is a cutting-edge compression technology optimised for a large variety of datasets, while RTL allows hardware vendors to use the reference design to produce hardware chips to allow the highest compression, lowest cost, and lowest power out of the algorithm.

3| Facebook

A few weeks ago, the popular social networking site open-sourced image and video algorithms. The photo-matching algorithm is called PDQ and the video-matching technology is called TMK+PDQF. Facebook announced the two technologies during the child safety hackathon which will help in detecting the graphical abusive contents, child exploitation, terrorist propaganda and other such threats.

These algorithms will be available on GitHub and are part of a suite of tools that Facebook uses to detect harmful content. The two technologies work in a method such that they will store the files in the form of short digital hashes and later comparing them with other instances in order to determine whether the files are identical and nearly identical images as well as videos to fight abuse on the internet platforms.

4| IBM

In July 2019, big blue open-sourced a deep learning algorithm known as PaccMann, stands for Prediction of anticancer compound sensitivity with Multi-modal attention-based neural networks. The goal of open-sourcing this algorithm is to deepen the understanding of cancer to equip industries and academia with the knowledge that could potentially which will help fuel new treatments and therapies. The researchers applied this algorithm to predict the sensitivity of cancer cell lines to known drugs and it achieves a superior predictive power compared to existing algorithms

Last month, the big blue took another huge step by open sourcing the POWER Instruction Set Architecture (ISA). The expectation behind this open-sourcing is to boost the IBM Power processor’s value by creating innovative hardware components. Besides this, the tech giant also contribute other technologies including a softcore implementation of the POWER ISA, as well as reference designs for the architecture-agnostic Open Coherent Accelerator Processor Interface (OpenCAPI) and the Open Memory Interface (OMI).

5| Google

In 2015, Google open-sourced the software library for TensorFlow which is an end-to-end open-source platform for machine learning. The idea behind this open sourcing is to let the machine learning community such as everyone from academic researchers, to engineers, to hobbyists—exchange ideas much more quickly, through working code rather than just research papers.

Conclusion

Open-sourcing tools and systems are making a huge impact on these companies. Previously, when there were almost no open-source systems or toolkits, companies created their products which had close data, it was difficult as well as time-consuming to enhance it or remove the errors and bugs. Currently, the potential of business relies upon sharing software source code ann which is why tech giants like Microsoft, Google, IBM, among others are open-sourcing their intelligent software. Also, AI is at its infancy stage and open-sourcing will not only make it easier to develop but also the developer’s community gets a chance to learn and understand what’s going behind the emerging technologies.